Data Analysis Prompts

ROLE: You are a data cleaning assistant helping standardize a spreadsheet dataset for analysis.

CONTEXT: Below is a sample of the dataset. Columns are: [LIST OF COLUMN NAMES]

[PASTE SAMPLE DATA HERE, INCLUDING KNOWN PROBLEM ROWS]

TASK:
1. Review each column and identify formatting inconsistencies (e.g. date formats, capitalization, whitespace, abbreviations, units)
2. Propose and apply a single standard format for each column: [SPECIFY TARGET FORMATS WHERE KNOWN, e.g. dates as YYYY-MM-DD]
3. Flag rows that look like duplicates or outliers, but do not delete them — mark them for my review instead
4. Do not alter these columns without flagging first: [COLUMNS REQUIRING REVIEW BEFORE CHANGES, e.g. customer name, ID numbers]

CONSTRAINTS:
- Preserve every original row unless I confirm a deletion
- Do not invent or infer missing values — leave them blank and flag them
- [ANY ADDITIONAL CONSTRAINT, e.g. keep a specific column's original casing]

OUTPUT FORMAT:
1. The cleaned dataset as a table
2. A change log listing each column, what inconsistency was found, and what standard was applied
3. A separate list of flagged rows (possible duplicates/outliers) with a one-line reason for each flag

Extract the following fields from the text below: [FIELD 1, FIELD 2, FIELD 3, FIELD 4].

Output rules:
- Return strictly a JSON array of objects, one per entity found.
- Use exactly these keys: [key_1, key_2, key_3, key_4].
- If a value is missing or unclear, use null. Never guess.
- Do not wrap the output in markdown code fences.
- Do not add any explanation before or after the JSON.

Text:
"""
[PASTE TEXT]
"""